Acoustic signature analysis and data fusion of vehicles based on acoustic sensor arrays

Abstract

Considerable interest has arisen in the recent years utilizing inexpensive acoustic sensors in the battlefield to perform targets of interest identification and classification. There are many advantages of using acoustic sensor arrays. They are low cost, and relatively have low power consumption. They require no line of sight and provide many capabilities for target detection, bearing estimation, target tracking, classification and identification. Furthermore, they can provide cueing for other sensors and multiple acoustic sensor responses can be combined and triangulated to localize an energy source target in the field. In practice, however, many environment noise factors affect their performance in detecting targets of interest reliably and accurate. In this paper, we have proposed a novel approach for detection, classification, and identification of moving target vehicles. The approach is based on <i>Singular Value Decomposition</i> (SVD) coupled with <i>Particle Filtering</i> (PF) technique. Using SVD dominant features of vehicle acoustic signatures are extracted efficiently. Then, these feature vectors are employed for robust identification and classification of target vehicles based on a particle filtering scheme. The performance of the proposed approach was evaluated based on a set of experimental acoustic data from multiple vehicle test-runs. It is demonstrated that the approach yields very promising results where an array of acoustic sensors are used to detect, identify and classify target vehicles in the field.